For background on AOS systems see Corrado et al., U.S. Pat. No. 5,482,314. Such systems produce a signal for input to the ADS, which if the occupant is out of position (OOP) or in a rear facing infant seat (RFIS) (in the front seat of a vehicle), the deployment of the airbag is aborted, deferred or otherwise controlled, as in SAS.
More recent studies have revealed that there is a class of slow speed automotive accidents causing injury to children, youngsters and frail adults. This usually occurs when the .DELTA.V of the "crash" is 18 miles per hour or less, where the occupant or RFIS is unbelted and the driver jams on the brake. The airbag deployment sensor experiences a G-force great enough to signal deployment. During these low speed accidents, the child or occupant typically has slid, or is sliding, forward into the IP and KOZ when the airbag deploys. The airbag deployment injures the child because it is too close, having intruded into the Keep Out Zone (KOZ).
Neural net algorithms have a wide range of successful useful applications in various technical fields, such as voice recognition, diagnostic systems and machine vision, and are generally well known in the art. Neural networks are sometimes used in system classification tasks for which not all the rules, system descriptions, or underlying equations are known. For such systems, the neural network is presented with a limited number of samples or "snapshots" (neural network inputs), and the known system outputs for those snapshots (neural network outputs). This input and output data is collectively known as the neural network "training set", and is used to train (or program, or "adapt") the neural network to the particular problem or system it is supposed to solve or identify. If the training set, the neural network architecture, and the training rules and parameters are chosen properly, a system capable of correctly identifying patterns of data typically not originally present in the training set can be built. Here lies the power of neural-network-based systems: they are able to generalize and infer correct results from a limited (original) training dataset.
The process of developing an adaptive type of neural net algorithm is typically a 3 step process: Step 1 is to generate a data set that contains the test cases the system is required to learn. Step 2 is to train the neural network to recognize the data and separate it into the required decision space. Step 3 is to is to evaluate the performance against data not included in the training data set used in step 1.